23 citations · 75 across the 11 of their papers we have counts for
3 papers · 1 filter
Unsupervised Information Obfuscation for Split Inference of Neural Networks
Mohammad Samragh, Hossein Hosseini, Aleksei Triastcyn +3
Splitting network computations between the edge device and a server enables low edge-compute inference of neural networks but might expose sensitive information about the test quer…
Cascade Weight Shedding in Deep Neural Networks: Benefits and Pitfalls for Network Pruning
Kambiz Azarian, Fatih Porikli
We report, for the first time, on the cascade weight shedding phenomenon in deep neural networks where in response to pruning a small percentage of a network's weights, a large per…
Learned Threshold Pruning
Kambiz Azarian, Yash Bhalgat, Jinwon Lee +1
This paper presents a novel differentiable method for unstructured weight pruning of deep neural networks. Our learned-threshold pruning (LTP) method learns per-layer thresholds vi…